Modeling now for precision later

How GNS wants to make pay-for-performance unnecessary

GNS Healthcare Inc. is building a platform that aims to make value-based payment models unnecessary by giving companies a way to know which patients will respond before they even launch the drug.

While drug companies and payers are entering conversations about how to structure reimbursement to ensure payers are compensated for treatments that don’t perform as promised, GNS is looking beyond, using machine learning to build models that can dial in the most responsive patient population from the start.

Part of the picture is the idea of virtual patients, designed via the integration of real world evidence and clinical data through GNS’s causal learning platform.

GNS’s reverse engineering and forward simulation (REFS) platform is a hypothesis-free, causal machine learning technology that uses Bayesian network inference to build disease models. As it brings in more data, it learns from the information and updates its parameters and models accordingly.

The platform leverages genomic, clinical and real world data from electronic medical records, wearable devices

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